HR: 0800h
AN: H41F-0477    [Abstracts]
TI: Innovative Methods for Integrating Knowledge for Long-Term Monitoring of Contaminated Groundwater Sites: Understanding Microorganism Communities and their Associated Hydrochemical Environment
AU: Mouser, P J
EM: pmouser@cems.uvm.edu
AF: University of Vermont, Department of Civil & Environmental Engineering, 213 Votey Building, Burlington, VT 05405 United States
AU: * Rizzo, D M
EM: drizzo@cems.uvm.edu
AF: University of Vermont, Department of Civil & Environmental Engineering, 213 Votey Building, Burlington, VT 05405 United States
AU: Druschel, G
EM: gregory.druschel@uvm.edu
AF: University of Vermont, Department of Geology, Delahanty Hall, Burlington, VT 05405 United States
AU: O'Grady, P
EM: Patrick.OGrady@uvm.edu
AF: University of Vermont, Departmnet of Biology, Ecology and Molecular Systematics, Burlington, VT 05405 United States
AU: Stevens, L
EM: lori.stevens@uvm.edu
AF: University of Vermont, Departmnet of Biology, Ecology and Molecular Systematics, Burlington, VT 05405 United States
AB: This interdisciplinary study integrates hydrochemical and genome-based data to estimate the redox processes occurring at long-term monitoring sites. Groundwater samples have been collected from a well-characterized landfill-leachate contaminated aquifer in northeastern New York. Primers from the 16S rDNA gene were used to amplify Bacteria and Archaea in groundwater taken from monitoring wells located in clean, fringe, and contaminated locations within the aquifer. PCR-amplified rDNA were digested with restriction enzymes to evaluate terminal restriction fragment length polymorphism (T-RFLP) community profiles. The rDNA was cloned, sequenced, and partial sequences were matched against known organisms using the NCBI Blast database. Phylogenetic trees and bootstrapping were used to identify classifications of organisms and compare the communities from clean, fringe, and contaminated locations. We used Artificial Neural Network (ANN) models to incorporate microbial data with hydrochemical information for improving our understanding of subsurface processes.
DE: 0520 Data analysis: algorithms and implementation
DE: 0555 Neural networks, fuzzy logic, machine learning
DE: 1831 Groundwater quality
DE: 9820 Techniques applicable in three or more fields
SC: Hydrology [H]
MN: Fall Meeting 2005